On the consistency of methane retrievals using the Total Carbon Column Observing Network (TCCON) and multiple spectroscopic databases

Abstract

Abstract. The next and current generations of methane-retrieving satellite instruments are reliant on the Total Carbon Column Observing Network (TCCON) for validation. Understanding the biases inherent in TCCON and satellite methane retrievals is as important now as when TCCON started in 2004. In this study we highlight possible biases between different methane products by assessing the retrievals of the main methane isotopologue 12CH4. Using the TCCON GGG2014 retrieval environment, retrievals are performed using five separate spectroscopic databases from four separate TCCON sites (namely, Ascension Island, Ny-Ålesund, Darwin and Tsukuba) over the course of a year. The spectroscopic databases include those native to TCCON, GGG2014 and GGG2020; the high-resolution transmission molecular absorption database 2016 (HITRAN2016); the Gestion et Etude des Informations Spectroscopiques Atmosphériques 2020 (GEISA2020) database; and the ESA Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS). We assess the biases in retrieving methane using the standard TCCON windows and the methane window used by the Sentinel-5 Precursor (S5P) TROPOspheric Ozone Monitoring Instrument (TROPOMI) for each of the different spectroscopic databases. By assessing the retrieved 12CH4 values from individual windows against the standard TCCON retrievals, we find bias values of between 0.05 and 2.5 times the retrieval noise limit. These values vary depending on the window and TCCON site, with Ascension Island showing the lowest biases (typically <0.5) and Ny-Ålesund or Tsukuba showing the largest. For the spectroscopic databases, GEISA2020 shows the largest biases, often greater than 1.5 across the TCCON sites and considered windows. The TROPOMI spectral window (4190–4340 cm−1) shows the largest biases of all the spectral windows, typically >1, for all spectroscopic databases, suggesting that further improvements in spectroscopic parameters are necessary. We further assess the sensitivity of these biases to locally changing atmospheric conditions such as the solar zenith angle (SZA), water vapour and temperature. We find evidence of significant non-linear relationships between the variation in local conditions and the retrieval biases based on regression analysis. In general, each site, database and window combination indicates different degrees of sensitivity, with GEISA2020 often showing the most sensitivity for all TCCON sites. Ny-Ålesund and Tsukuba show the most sensitivity to variations in local condition, while Ascension Island indicates limited sensitivity. Finally, we investigate the biases associated with retrieving 13CH4 from each TCCON site and spectroscopic database, through the calculation of δ13C values. We find high levels of inconsistency, in some cases >100 ‰ between databases, suggesting more work is required to refine the spectroscopic parameters of 13CH4.

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

“…Among them, x i , y i , and z i are arbitrary measures, and rand ð0, 1Þ is the random control measure selection function. Randomly select the causes of sports injure, carry out crossanalysis [9], and update the analysis degree. Under the constraint of matching degree, the optimal measure conforming to the weight is obtained through the combination form, and the calculation process is shown in Formula (5).…”

“…Multivariate regression is to realize the optimum of rowing competition in the form of probability p i and combination and carry out neighborhood analysis on the optimal measures to obtain the final measures that meet the standards [9]. The calculation process is shown in Formula (6).…”